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neerajprad
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Commit
•
ede35d1
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Parent(s):
e6ee1e5
Colab update - adding model files.
Browse files- app.py +28 -0
- requirements.txt +2 -0
- saved_model_files/config.json +34 -0
- saved_model_files/preprocessor_config.json +17 -0
- saved_model_files/pytorch_model.bin +3 -0
- saved_model_files/training_args.bin +3 -0
app.py
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from datasets import load_dataset
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import gradio as gr
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from transformers import AutoFeatureExtractor, AutoModelForImageClassification
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# This should be the same as the first line of Python code in this Colab notebook
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dataset = load_dataset('beans')
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extractor = AutoFeatureExtractor.from_pretrained("saved_model_files")
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model = AutoModelForImageClassification.from_pretrained("saved_model_files")
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labels = dataset['train'].features['labels'].names
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def classify(im):
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features = feature_extractor(im, return_tensors='pt')
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inp = model(**features)
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logits = torch.nn.functional.softmax(inp.logits, dim=-1)
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probability = torch.nn.functional.softmax(logits, dim=-1)
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probs = probability[0].detach().numpy()
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confidences = {label: float(probs[i]) for i, label in enumerate(labels)}
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return confidences
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interface = gr.Interface(
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fn=classify,
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inputs=gr.Image(shape=(224, 224)),
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outputs=gr.Label(num_top_classes=3),
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)
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interface.launch(debug=True, share=True)
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requirements.txt
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torch
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transformers
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saved_model_files/config.json
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{
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"_name_or_path": "google/vit-base-patch16-224",
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"architectures": [
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"ViTForImageClassification"
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],
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"attention_probs_dropout_prob": 0.0,
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"encoder_stride": 16,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.0,
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"hidden_size": 768,
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"id2label": {
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"0": "angular_leaf_spot",
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"1": "bean_rust",
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"2": "healthy"
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},
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"image_size": 224,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"angular_leaf_spot": "0",
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"bean_rust": "1",
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"healthy": "2"
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},
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"layer_norm_eps": 1e-12,
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"model_type": "vit",
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"num_attention_heads": 12,
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"num_channels": 3,
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"num_hidden_layers": 12,
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"patch_size": 16,
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"problem_type": "single_label_classification",
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"qkv_bias": true,
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"torch_dtype": "float32",
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"transformers_version": "4.22.1"
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}
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saved_model_files/preprocessor_config.json
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{
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"do_normalize": true,
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"do_resize": true,
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"feature_extractor_type": "ViTFeatureExtractor",
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"image_mean": [
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0.5,
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0.5,
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0.5
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],
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"image_std": [
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0.5,
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0.5,
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0.5
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],
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"resample": 2,
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"size": 224
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}
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saved_model_files/pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:3aee5d5906d9d0af9dd86732798188604614b6421d14eab6d834c63ce3e9135c
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size 343270065
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saved_model_files/training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:9adfb02956cac9ac034ff89cd10fc02b842ffc71d4d828163ba2f42d61d54e4a
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size 3375
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